Dealing with Missing Data in an IPD Meta‐Analysis

Thomas P. A. Debray, Kym I. E. Snell, Matteo Quartagno, Shahab Jolani, K.G.M. Moons, Richard D. Riley

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic


This chapter provides an overview of different methods for dealing with missing data in an individual participant data (IPD) meta-analysis. It highlights the specific challenges of dealing with missing data in an IPD meta-analysis context, including how to preserve the clustering of participants within primary studies, whilst allowing for potential between-study heterogeneity. The describes the various types of missing data that can occur in an IPD meta-analysis project, and the strategies, statistical approaches and software to deal with each. It focuses on dealing with missing data in the context of IPD meta-analyses of observational studies, for example for examining prognostic factors or developing prediction models. A number of prognostic factors (‘predictors’) are known to be associated with the incidence of preeclampsia; for example, a woman has a higher risk if she had pre-eclampsia in a previous pregnancy, or if there is a family history of pre-eclampsia, diabetes, or renal disease.

Original languageEnglish
Title of host publicationIndividual Participant Data Meta‐Analysis
Subtitle of host publicationA Handbook for Healthcare Research
EditorsRichard D. Riley, Jayne F. Tierney, Lesley A. Stewart
ISBN (Print)978-1-119-33372-2
Publication statusPublished - 2021

Publication series

SeriesStatistics in Practice Series


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